A clustering-based failure mode and effect analysis model and its application to the edible bird nest industry

Failure mode and effect analysis (FMEA) is a popular safety and reliability analysis tool in examining potential failures of products, process, designs, or services, in a wide range of industries. While FMEA is a popular tool, the limitations of the traditional Risk Priority Number (RPN) model in FMEA have been highlighted in the literature. Even though many alternatives to the traditional RPN model have been proposed, there are not many investigations on the use of clustering techniques in FMEA. The main aim of this paper was to examine the use of a new Euclidean distance-based similarity measure and an incremental-learning clustering model, i.e., fuzzy adaptive resonance theory neural network, for similarity analysis and clustering of failure modes in FMEA; therefore, allowing the failure modes to be analyzed, visualized, and clustered. In this paper, the concept of a risk interval encompassing a group of failure modes is investigated. Besides that, a new approach to analyze risk ordering of different failure groups is introduced. These proposed methods are evaluated using a case study related to the edible bird nest industry in Sarawak, Malaysia. In short, the contributions of this paper are threefold: (1) a new Euclidean distance-based similarity measure, (2) a new risk interval measure for a group of failure modes, and (3) a new analysis of risk ordering of different failure groups

A clustering-based failure mode and effect analysis model and its application to the edible bird nest industry

Failure mode and effect analysis (FMEA) is a popular safety and reliability analysis tool in examining potential failures of products, process, designs, or services, in a wide range of industries. While FMEA is a popular tool, the limitations of the traditional Risk Priority Number (RPN) model in FMEA have been highlighted in the literature. Even though many alternatives to the traditional RPN model have been proposed, there are not many investigations on the use of clustering techniques in FMEA. The main aim of this paper was to examine the use of a new Euclidean distance-based similarity measure and an incremental-learning clustering model, i.e., fuzzy adaptive resonance theory neural network, for similarity analysis and clustering of failure modes in FMEA; therefore, allowing the failure modes to be analyzed, visualized, and clustered. In this paper, the concept of a risk interval encompassing a group of failure modes is investigated. Besides that, a new approach to analyze risk ordering of different failure groups is introduced. These proposed methods are evaluated using a case study related to the edible bird nest industry in Sarawak, Malaysia. In short, the contributions of this paper are threefold: (1) a new Euclidean distance-based similarity measure, (2) a new risk interval measure for a group of failure modes, and (3) a new analysis of risk ordering of different failure groups

Quantitative and Qualitative Models for Managing Risk Interdependencies in Supply Chain

The interdependent nature of supply chain elements and events requires risk systems must be assessed as an interrelated framework to optimize their management and integrate effectively with other decision-making tools in uncertain environments. This research shows a synthesis and analysis of the main qualitative/quantitative methods that have been used in the literature considering the treatment of event dependencies in supply chain risk management in the period 2003– 2018. The results revealed that the integration with disruption analysis tools and artificial intelligence methods are the most common types adopted, with increasing trend and effectiveness of Bayesian and fuzzy theory approache

Authentication of Edible Bird's Nest using Advanced Analytical Techniques and Multivariate Data Analysis

Ph.DDOCTOR OF PHILOSOPH

Hybrid-fuzzy techniques with flexibility and attitudinal parameters for supporting early product design and reliability management

The main aim of the research work presented in this thesis is to define and develop novel Hybrid Fuzzy-based techniques for supporting aspects of product development engineering, specifically product reliability at the early phase of product design under the design for reliability philosophy and concept designs assessment problems when the required information is rough and incomplete. Thus, to achieve the above-stated aim, which has been formulated in the effort to filling the identified gaps in the literature which comprise of the need for a holistic, flexible and adjustable method to facilitate and support product design concept assessment and product reliability at the early product design phase. The need for the incorporation of the attitudinal character of the DMs into the product reliability and design concept assessment and finally, the need to account for the several interrelated complex attributes in the product reliability and design concept assessment process. A combination of research methods has been employed which includes an extensive literature review, multiple case study approach, and personal interview of experts, through which data were, collected that provided information for the real-life case study. With the new Hybrid Fuzzy-based techniques (i.e. the intuitionistic fuzzy TOPSIS model which is based on an exponential-related function (IF-TOPSISEF) and the Multi-attribute group decision-making (MAGDM) method which is based on a generalized triangular intuitionistic fuzzy geometric averaging (GTIFGA) operator), a more robust method for the product reliability and design concepts assessment respectively have been achieved as displayed in the comparative analysis in the thesis. The new methods have provided a more complete and a holistic view of the assessment process, by looking at the product reliability and design concept assessment from different scenario depending on the interest of the DMs. Using the above methods, the thesis has been able to evaluated some complex mechanical systems in literature and in real-life including Crawler Crane Machine and Forklift Truck for design change with the purpose of gaining appropriate reliability knowledge and information needed at the early product design phase, and that can subsequently aid and improve the product design concepts after all such useful information have been added into the new design. With the application of the new methods, and their proven feasibility and rationality as displayed in the assessment results of the complex mechanical systems in literature and that of the real-life case studies, this thesis, therefore, can conclude that the Hybrid Fuzzy-based techniques proposed, has provided a better and a novel alternative to existing product reliability and design concepts assessment methods

Ecological Outcomes of Movement Behavior in Brown Pelicans from the South Atlantic Bight

Vagile organisms are expected to display movement behaviors that respond to a wide variety of both intrinsic and extrinsic factors. Identifying drivers of movement is fundamental to understanding the ecology of species, as well as implementing effective conservation measures. Technological advancements have allowed for the collection of fine-scale positional data at rapid temporal scales, which can be a powerful tool for assessing the movement behavior of tracked species and for understanding the potential fitness implications resulting from variations in animal space use. The goal of this dissertation was to identify important drivers of movement behavior and to describe the ecological outcomes of movement decisions in Eastern brown pelicans (Pelecanus occidentalis carolinensis) from the South Atlantic Bight. A total of 86 individual pelicans were outfitted with solar-powered GPS satellite transmitters in coastal South Carolina and Georgia, USA, from 2017 – 2020. Two cohorts of pelicans tracked during the passage of three tropical cyclones demonstrated a reduction in movement correlated with anomalies in barometric pressure and wind speed relative to ambient conditions, indicating a shelter-and-wait strategy for increasing survival during these extreme weather events. By measuring the concentrations of an environmental contaminant, poly- and perfluoroalkyl substances, in the eggs of pelicans from three colonies located near Charleston, South Carolina, I demonstrated that eggs contained relatively elevated concentrations of chemicals regardless of proximity to likely point sources. GPS tracking of adults from the same colonies further suggested that variations in urban habitat use for foraging adults during the breeding season were also not reflected in egg contaminant concentrations. In contrast, the relative risk to foraging adult pelicans of encountering surface oil from a ship-based spill near Charleston Harbor was significantly influenced by location, as demonstrated through the use of an oil spill modeling toolkit combined with pelican telemetry data. Finally, the partial migration strategy of brown pelicans in the South Atlantic Bight is likely maintained by the ontogenetic migration of their primary prey, Atlantic menhaden (Brevoortia tyrannus), and aligns with the fasting endurance hypothesis of partial migration. Understanding the causes and consequences of movement in brown pelicans in the South Atlantic Bight has important implications for the ecology and conservation of this species throughout their range